This paper proposes an accurate and computationally efficient solver of the heat equation on a volumetric domain, through the (r,r)-degree Padé-Chebyshev rational approximation of the ex- ponential representation of the solution. To this end, the heat diffusion problem is converted to a set of r differential equations, which involve only the Laplace-Beltrami operator. The discrete heat equation is equivalent to r sparse, symmetric linear systems and is independent of the volume discretization as a tetrahedral mesh or a regular grid, the evaluation of the Laplacian spectrum, and the selection of a subset of eigenpairs. Our approach has a super-linear computational cost, is free of user-defined parameters, and has an approximation accuracy lower than 10^(-r). Finally, we propose a simple criterion to select the time value that provides the best compromise between approximation accuracy and smoothness of the solution.
Volumetric heat Kernel: Padé-Chebyshev approximation, convergence, and computation
2015
Abstract
This paper proposes an accurate and computationally efficient solver of the heat equation on a volumetric domain, through the (r,r)-degree Padé-Chebyshev rational approximation of the ex- ponential representation of the solution. To this end, the heat diffusion problem is converted to a set of r differential equations, which involve only the Laplace-Beltrami operator. The discrete heat equation is equivalent to r sparse, symmetric linear systems and is independent of the volume discretization as a tetrahedral mesh or a regular grid, the evaluation of the Laplacian spectrum, and the selection of a subset of eigenpairs. Our approach has a super-linear computational cost, is free of user-defined parameters, and has an approximation accuracy lower than 10^(-r). Finally, we propose a simple criterion to select the time value that provides the best compromise between approximation accuracy and smoothness of the solution.File | Dimensione | Formato | |
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Descrizione: Volumetric heat Kernel: Padé-Chebyshev approximation, convergence, and computation
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